A Note on Data-Driven Contaminant Simulation

Abstract.
In this paper we introduce a numerical procedure for performing
dynamic data driven simulations (DDDAS). The main ingredient
of our simulation is the multiscale interpolation technique that maps
the sensor data into the solution space. We test our method on various
synthetic examples. In particular we show that frequent updating of the
sensor data in the simulations can significantly improve the prediction
results and thus important for applications. The frequency of sensor data
updating in the simulations is related to streaming capabilities and addressed
within DDDAS framework. A further extension of our approach
using local inversion is also discussed.